Solar radiation has long been recognized as a major component of the energy budget of streams, and modeling of stream temperature across stream basins requires estimates of riparian stream shade over extensive areas. A variety of methods are available for measuring shade locally, including hemispherical photography, however these are impractical for distributed stream temperature modeling. Methods for estimating spatially distributed shade are rarer. Algorithms requiring estimates of tree heights have been used, but estimates of tree heights distributed across stream basins are often inaccurate. We explored the use of remote sensing data to more directly estimate stream shade by developing relationships between geographically registered hemispherical photography and satellite imagery. Heterogeneity in vegetation families yielded poor relationships between shade and NDVI. Classification into categories of open/grass, shrub communities, and conifer communities, was more successful in discriminating shade measurements. Shade from riparian vegetation is affected by a variety of natural and anthropogenic disturbances, and the Boise river basin has experienced several wildfires and post-fire debris flows, providing an opportunity to validate the shade estimates against observations of disturbance. We further evaluated the shade estimates against stream temperature data from 10 streams with varying levels of disturbance. The strong correlations suggest the possibility of substantially improving empirical models of stream temperature using remote sensing data. The methods also hold promise for use in distributed physically based stream temperature models.